Overview

Dataset statistics

Number of variables15
Number of observations10840
Missing cells4542
Missing cells (%)2.8%
Duplicate rows410
Duplicate rows (%)3.8%
Total size in memory1.2 MiB
Average record size in memory120.0 B

Variable types

Categorical7
Numeric8

Alerts

Dataset has 410 (3.8%) duplicate rowsDuplicates
App has a high cardinality: 9659 distinct valuesHigh cardinality
Genres has a high cardinality: 119 distinct valuesHigh cardinality
Current Ver has a high cardinality: 2831 distinct valuesHigh cardinality
Reviews is highly overall correlated with InstallsHigh correlation
Installs is highly overall correlated with ReviewsHigh correlation
Type is highly imbalanced (62.0%)Imbalance
Content Rating is highly imbalanced (61.5%)Imbalance
Rating has 1474 (13.6%) missing valuesMissing
Size has 1695 (15.6%) missing valuesMissing
Android Ver has 1364 (12.6%) missing valuesMissing
Price is highly skewed (γ1 = 23.70739238)Skewed
App is uniformly distributedUniform
Reviews has 596 (5.5%) zerosZeros
Price has 10040 (92.6%) zerosZeros

Reproduction

Analysis started2023-05-15 11:48:48.149149
Analysis finished2023-05-15 11:49:02.720291
Duration14.57 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

App
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct9659
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
ROBLOX
 
9
CBS Sports App - Scores, News, Stats & Watch Live
 
8
ESPN
 
7
Duolingo: Learn Languages Free
 
7
Candy Crush Saga
 
7
Other values (9654)
10802 

Length

Max length194
Median length75
Mean length22.517435
Min length1

Characters and Unicode

Total characters244089
Distinct characters478
Distinct categories18 ?
Distinct scripts16 ?
Distinct blocks27 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8861 ?
Unique (%)81.7%

Sample

1st rowPhoto Editor & Candy Camera & Grid & ScrapBook
2nd rowColoring book moana
3rd rowU Launcher Lite – FREE Live Cool Themes, Hide Apps
4th rowSketch - Draw & Paint
5th rowPixel Draw - Number Art Coloring Book

Common Values

ValueCountFrequency (%)
ROBLOX 9
 
0.1%
CBS Sports App - Scores, News, Stats & Watch Live 8
 
0.1%
ESPN 7
 
0.1%
Duolingo: Learn Languages Free 7
 
0.1%
Candy Crush Saga 7
 
0.1%
8 Ball Pool 7
 
0.1%
slither.io 6
 
0.1%
Bubble Shooter 6
 
0.1%
Zombie Catchers 6
 
0.1%
Bleacher Report: sports news, scores, & highlights 6
 
0.1%
Other values (9649) 10771
99.4%

Length

2023-05-15T11:49:02.874353image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2823
 
6.6%
for 560
 
1.3%
free 514
 
1.2%
app 332
 
0.8%
and 283
 
0.7%
the 270
 
0.6%
mobile 222
 
0.5%
news 195
 
0.5%
video 194
 
0.5%
live 194
 
0.5%
Other values (9548) 36991
86.9%

Most occurring characters

ValueCountFrequency (%)
31738
 
13.0%
e 19418
 
8.0%
a 14836
 
6.1%
o 13636
 
5.6%
r 13241
 
5.4%
i 12140
 
5.0%
t 10334
 
4.2%
n 9846
 
4.0%
s 8716
 
3.6%
l 8546
 
3.5%
Other values (468) 101638
41.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 154480
63.3%
Uppercase Letter 47206
 
19.3%
Space Separator 31739
 
13.0%
Other Punctuation 4082
 
1.7%
Dash Punctuation 2675
 
1.1%
Decimal Number 2381
 
1.0%
Close Punctuation 350
 
0.1%
Open Punctuation 346
 
0.1%
Other Letter 341
 
0.1%
Other Symbol 232
 
0.1%
Other values (8) 257
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 13
 
3.8%
ر 9
 
2.6%
ل 7
 
2.1%
6
 
1.8%
6
 
1.8%
ق 5
 
1.5%
ب 5
 
1.5%
ح 5
 
1.5%
5
 
1.5%
4
 
1.2%
Other values (214) 276
80.9%
Lowercase Letter
ValueCountFrequency (%)
e 19418
12.6%
a 14836
 
9.6%
o 13636
 
8.8%
r 13241
 
8.6%
i 12140
 
7.9%
t 10334
 
6.7%
n 9846
 
6.4%
s 8716
 
5.6%
l 8546
 
5.5%
c 4924
 
3.2%
Other values (71) 38843
25.1%
Uppercase Letter
ValueCountFrequency (%)
C 4227
 
9.0%
S 3850
 
8.2%
A 3236
 
6.9%
B 3040
 
6.4%
P 2875
 
6.1%
F 2839
 
6.0%
D 2805
 
5.9%
T 2716
 
5.8%
M 2705
 
5.7%
E 2500
 
5.3%
Other values (39) 16413
34.8%
Other Symbol
ValueCountFrequency (%)
99
42.7%
® 56
24.1%
🔥 6
 
2.6%
🍀 6
 
2.6%
5
 
2.2%
5
 
2.2%
4
 
1.7%
° 4
 
1.7%
3
 
1.3%
🎨 3
 
1.3%
Other values (34) 41
17.7%
Other Punctuation
ValueCountFrequency (%)
: 999
24.5%
& 979
24.0%
, 901
22.1%
. 587
14.4%
' 225
 
5.5%
! 150
 
3.7%
/ 128
 
3.1%
? 39
 
1.0%
# 17
 
0.4%
" 13
 
0.3%
Other values (9) 44
 
1.1%
Decimal Number
ValueCountFrequency (%)
0 463
19.4%
2 452
19.0%
1 440
18.5%
3 279
11.7%
8 203
8.5%
4 175
 
7.3%
5 112
 
4.7%
7 111
 
4.7%
6 79
 
3.3%
9 67
 
2.8%
Math Symbol
ValueCountFrequency (%)
+ 133
67.9%
| 51
 
26.0%
~ 5
 
2.6%
> 2
 
1.0%
× 1
 
0.5%
1
 
0.5%
1
 
0.5%
1
 
0.5%
1
 
0.5%
Nonspacing Mark
ValueCountFrequency (%)
5
38.5%
2
 
15.4%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
Spacing Mark
ValueCountFrequency (%)
4
26.7%
2
13.3%
ি 2
13.3%
2
13.3%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 2502
93.5%
146
 
5.5%
21
 
0.8%
2
 
0.1%
2
 
0.1%
1
 
< 0.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 321
91.7%
] 22
 
6.3%
3
 
0.9%
3
 
0.9%
1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 317
91.6%
[ 22
 
6.4%
3
 
0.9%
3
 
0.9%
1
 
0.3%
Final Punctuation
ValueCountFrequency (%)
19
90.5%
1
 
4.8%
» 1
 
4.8%
Space Separator
ValueCountFrequency (%)
31738
> 99.9%
  1
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 201621
82.6%
Common 42034
 
17.2%
Arabic 86
 
< 0.1%
Hangul 76
 
< 0.1%
Han 70
 
< 0.1%
Katakana 59
 
< 0.1%
Cyrillic 58
 
< 0.1%
Bengali 18
 
< 0.1%
Khmer 13
 
< 0.1%
Ethiopic 13
 
< 0.1%
Other values (6) 41
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
31738
75.5%
- 2502
 
6.0%
: 999
 
2.4%
& 979
 
2.3%
, 901
 
2.1%
. 587
 
1.4%
0 463
 
1.1%
2 452
 
1.1%
1 440
 
1.0%
) 321
 
0.8%
Other values (98) 2652
 
6.3%
Latin
ValueCountFrequency (%)
e 19418
 
9.6%
a 14836
 
7.4%
o 13636
 
6.8%
r 13241
 
6.6%
i 12140
 
6.0%
t 10334
 
5.1%
n 9846
 
4.9%
s 8716
 
4.3%
l 8546
 
4.2%
c 4924
 
2.4%
Other values (87) 85984
42.6%
Han
ValueCountFrequency (%)
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
1
 
1.4%
1
 
1.4%
1
 
1.4%
Other values (52) 52
74.3%
Hangul
ValueCountFrequency (%)
6
 
7.9%
4
 
5.3%
3
 
3.9%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (47) 48
63.2%
Katakana
ValueCountFrequency (%)
6
 
10.2%
5
 
8.5%
4
 
6.8%
3
 
5.1%
3
 
5.1%
3
 
5.1%
3
 
5.1%
3
 
5.1%
2
 
3.4%
2
 
3.4%
Other values (24) 25
42.4%
Cyrillic
ValueCountFrequency (%)
и 6
 
10.3%
е 5
 
8.6%
о 4
 
6.9%
с 3
 
5.2%
в 3
 
5.2%
л 3
 
5.2%
т 3
 
5.2%
Р 3
 
5.2%
к 2
 
3.4%
н 2
 
3.4%
Other values (17) 24
41.4%
Arabic
ValueCountFrequency (%)
ا 13
15.1%
ر 9
 
10.5%
ل 7
 
8.1%
ق 5
 
5.8%
ب 5
 
5.8%
ح 5
 
5.8%
ع 4
 
4.7%
و 4
 
4.7%
ن 4
 
4.7%
ف 4
 
4.7%
Other values (16) 26
30.2%
Ethiopic
ValueCountFrequency (%)
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
Other values (3) 3
23.1%
Bengali
ValueCountFrequency (%)
4
22.2%
ি 2
11.1%
2
11.1%
2
11.1%
2
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Khmer
ValueCountFrequency (%)
2
15.4%
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
Devanagari
ValueCountFrequency (%)
2
18.2%
2
18.2%
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
Hiragana
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Greek
ValueCountFrequency (%)
σ 2
28.6%
β 1
14.3%
Α 1
14.3%
Ε 1
14.3%
Κ 1
14.3%
Σ 1
14.3%
Myanmar
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Hebrew
ValueCountFrequency (%)
ה 1
20.0%
פ 1
20.0%
ק 1
20.0%
ו 1
20.0%
ת 1
20.0%
Inherited
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 242988
99.5%
None 329
 
0.1%
Punctuation 197
 
0.1%
Letterlike Symbols 100
 
< 0.1%
Arabic 86
 
< 0.1%
Hangul 76
 
< 0.1%
CJK 70
 
< 0.1%
Katakana 67
 
< 0.1%
Cyrillic 58
 
< 0.1%
Bengali 18
 
< 0.1%
Other values (17) 100
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31738
 
13.1%
e 19418
 
8.0%
a 14836
 
6.1%
o 13636
 
5.6%
r 13241
 
5.4%
i 12140
 
5.0%
t 10334
 
4.3%
n 9846
 
4.1%
s 8716
 
3.6%
l 8546
 
3.5%
Other values (77) 100537
41.4%
Punctuation
ValueCountFrequency (%)
146
74.1%
21
 
10.7%
19
 
9.6%
5
 
2.5%
2
 
1.0%
1
 
0.5%
1
 
0.5%
1
 
0.5%
1
 
0.5%
Letterlike Symbols
ValueCountFrequency (%)
99
99.0%
1
 
1.0%
None
ValueCountFrequency (%)
® 56
 
17.0%
é 33
 
10.0%
í 19
 
5.8%
á 19
 
5.8%
ı 16
 
4.9%
İ 14
 
4.3%
· 10
 
3.0%
ü 8
 
2.4%
ú 6
 
1.8%
🔥 6
 
1.8%
Other values (80) 142
43.2%
Arabic
ValueCountFrequency (%)
ا 13
15.1%
ر 9
 
10.5%
ل 7
 
8.1%
ق 5
 
5.8%
ب 5
 
5.8%
ح 5
 
5.8%
ع 4
 
4.7%
و 4
 
4.7%
ن 4
 
4.7%
ف 4
 
4.7%
Other values (16) 26
30.2%
Katakana
ValueCountFrequency (%)
8
 
11.9%
6
 
9.0%
5
 
7.5%
4
 
6.0%
3
 
4.5%
3
 
4.5%
3
 
4.5%
3
 
4.5%
3
 
4.5%
2
 
3.0%
Other values (25) 27
40.3%
Hangul
ValueCountFrequency (%)
6
 
7.9%
4
 
5.3%
3
 
3.9%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (47) 48
63.2%
Cyrillic
ValueCountFrequency (%)
и 6
 
10.3%
е 5
 
8.6%
о 4
 
6.9%
с 3
 
5.2%
в 3
 
5.2%
л 3
 
5.2%
т 3
 
5.2%
Р 3
 
5.2%
к 2
 
3.4%
н 2
 
3.4%
Other values (17) 24
41.4%
Dingbats
ValueCountFrequency (%)
5
50.0%
3
30.0%
2
 
20.0%
Misc Symbols
ValueCountFrequency (%)
5
45.5%
4
36.4%
2
 
18.2%
VS
ValueCountFrequency (%)
5
100.0%
Bengali
ValueCountFrequency (%)
4
22.2%
ি 2
11.1%
2
11.1%
2
11.1%
2
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
CJK
ValueCountFrequency (%)
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
1
 
1.4%
1
 
1.4%
1
 
1.4%
Other values (52) 52
74.3%
Khmer
ValueCountFrequency (%)
2
15.4%
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
IPA Ext
ValueCountFrequency (%)
ə 2
100.0%
Latin Ext Additional
ValueCountFrequency (%)
2
66.7%
1
33.3%
Geometric Shapes
ValueCountFrequency (%)
2
66.7%
1
33.3%
Hiragana
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Devanagari
ValueCountFrequency (%)
2
18.2%
2
18.2%
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
Myanmar
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Misc Technical
ValueCountFrequency (%)
1
100.0%
Emoticons
ValueCountFrequency (%)
😍 1
20.0%
😂 1
20.0%
😄 1
20.0%
😜 1
20.0%
😘 1
20.0%
Math Operators
ValueCountFrequency (%)
1
50.0%
1
50.0%
Ethiopic
ValueCountFrequency (%)
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
Other values (3) 3
23.1%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇺 1
50.0%
🇸 1
50.0%
Arrows
ValueCountFrequency (%)
1
100.0%
Hebrew
ValueCountFrequency (%)
ה 1
20.0%
פ 1
20.0%
ק 1
20.0%
ו 1
20.0%
ת 1
20.0%

Category
Categorical

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
FAMILY
1972 
GAME
1144 
TOOLS
843 
MEDICAL
 
463
BUSINESS
 
460
Other values (28)
5958 

Length

Max length19
Median length16
Mean length9.0244465
Min length4

Characters and Unicode

Total characters97825
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowART_AND_DESIGN
2nd rowART_AND_DESIGN
3rd rowART_AND_DESIGN
4th rowART_AND_DESIGN
5th rowART_AND_DESIGN

Common Values

ValueCountFrequency (%)
FAMILY 1972
18.2%
GAME 1144
 
10.6%
TOOLS 843
 
7.8%
MEDICAL 463
 
4.3%
BUSINESS 460
 
4.2%
PRODUCTIVITY 424
 
3.9%
PERSONALIZATION 392
 
3.6%
COMMUNICATION 387
 
3.6%
SPORTS 384
 
3.5%
LIFESTYLE 382
 
3.5%
Other values (23) 3989
36.8%

Length

2023-05-15T11:49:03.104005image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
family 1972
18.2%
game 1144
 
10.6%
tools 843
 
7.8%
medical 463
 
4.3%
business 460
 
4.2%
productivity 424
 
3.9%
personalization 392
 
3.6%
communication 387
 
3.6%
sports 384
 
3.5%
lifestyle 382
 
3.5%
Other values (23) 3989
36.8%

Most occurring characters

ValueCountFrequency (%)
A 10424
 
10.7%
I 8783
 
9.0%
E 7958
 
8.1%
N 7335
 
7.5%
O 7125
 
7.3%
S 6558
 
6.7%
L 6189
 
6.3%
T 5894
 
6.0%
M 5155
 
5.3%
_ 3575
 
3.7%
Other values (14) 28829
29.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 94250
96.3%
Connector Punctuation 3575
 
3.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 10424
11.1%
I 8783
 
9.3%
E 7958
 
8.4%
N 7335
 
7.8%
O 7125
 
7.6%
S 6558
 
7.0%
L 6189
 
6.6%
T 5894
 
6.3%
M 5155
 
5.5%
D 3556
 
3.8%
Other values (13) 25273
26.8%
Connector Punctuation
ValueCountFrequency (%)
_ 3575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94250
96.3%
Common 3575
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 10424
11.1%
I 8783
 
9.3%
E 7958
 
8.4%
N 7335
 
7.8%
O 7125
 
7.6%
S 6558
 
7.0%
L 6189
 
6.6%
T 5894
 
6.3%
M 5155
 
5.5%
D 3556
 
3.8%
Other values (13) 25273
26.8%
Common
ValueCountFrequency (%)
_ 3575
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 10424
 
10.7%
I 8783
 
9.0%
E 7958
 
8.1%
N 7335
 
7.5%
O 7125
 
7.3%
S 6558
 
6.7%
L 6189
 
6.3%
T 5894
 
6.0%
M 5155
 
5.3%
_ 3575
 
3.7%
Other values (14) 28829
29.5%

Rating
Real number (ℝ)

Distinct39
Distinct (%)0.4%
Missing1474
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean4.1917574
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.8 KiB
2023-05-15T11:49:03.288318image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.2
Q14
median4.3
Q34.5
95-th percentile4.8
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.51521886
Coefficient of variation (CV)0.12291237
Kurtosis5.7869241
Mean4.1917574
Median Absolute Deviation (MAD)0.2
Skewness-1.8496951
Sum39260
Variance0.26545047
MonotonicityNot monotonic
2023-05-15T11:49:03.468172image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4.4 1109
10.2%
4.3 1076
9.9%
4.5 1038
9.6%
4.2 952
8.8%
4.6 823
7.6%
4.1 708
 
6.5%
4 568
 
5.2%
4.7 499
 
4.6%
3.9 386
 
3.6%
3.8 303
 
2.8%
Other values (29) 1904
17.6%
(Missing) 1474
13.6%
ValueCountFrequency (%)
1 16
0.1%
1.2 1
 
< 0.1%
1.4 3
 
< 0.1%
1.5 3
 
< 0.1%
1.6 4
 
< 0.1%
1.7 8
0.1%
1.8 8
0.1%
1.9 13
0.1%
2 12
0.1%
2.1 8
0.1%
ValueCountFrequency (%)
5 274
 
2.5%
4.9 87
 
0.8%
4.8 234
 
2.2%
4.7 499
4.6%
4.6 823
7.6%
4.5 1038
9.6%
4.4 1109
10.2%
4.3 1076
9.9%
4.2 952
8.8%
4.1 708
6.5%

Reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6001
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean444152.9
Minimum0
Maximum78158306
Zeros596
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size84.8 KiB
2023-05-15T11:49:03.687807image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138
median2094
Q354775.5
95-th percentile1462042.6
Maximum78158306
Range78158306
Interquartile range (IQR)54737.5

Descriptive statistics

Standard deviation2927760.6
Coefficient of variation (CV)6.5917855
Kurtosis341.06036
Mean444152.9
Median Absolute Deviation (MAD)2094
Skewness16.449584
Sum4.8146174 × 109
Variance8.5717822 × 1012
MonotonicityNot monotonic
2023-05-15T11:49:03.889819image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 596
 
5.5%
1 272
 
2.5%
2 214
 
2.0%
3 175
 
1.6%
4 137
 
1.3%
5 108
 
1.0%
6 97
 
0.9%
7 90
 
0.8%
8 74
 
0.7%
9 65
 
0.6%
Other values (5991) 9012
83.1%
ValueCountFrequency (%)
0 596
5.5%
1 272
2.5%
2 214
 
2.0%
3 175
 
1.6%
4 137
 
1.3%
5 108
 
1.0%
6 97
 
0.9%
7 90
 
0.8%
8 74
 
0.7%
9 65
 
0.6%
ValueCountFrequency (%)
78158306 1
< 0.1%
78128208 1
< 0.1%
69119316 2
< 0.1%
69109672 1
< 0.1%
66577446 1
< 0.1%
66577313 2
< 0.1%
66509917 1
< 0.1%
56646578 1
< 0.1%
56642847 2
< 0.1%
44893888 1
< 0.1%

Size
Real number (ℝ)

Distinct459
Distinct (%)5.0%
Missing1695
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean21506.534
Minimum10
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.8 KiB
2023-05-15T11:49:04.105912image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile1400
Q14900
median13000
Q330000
95-th percentile73000
Maximum100000
Range99990
Interquartile range (IQR)25100

Descriptive statistics

Standard deviation22596.021
Coefficient of variation (CV)1.0506584
Kurtosis1.9207665
Mean21506.534
Median Absolute Deviation (MAD)9700
Skewness1.5564771
Sum1.9667725 × 108
Variance5.1058016 × 108
MonotonicityNot monotonic
2023-05-15T11:49:04.302829image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11000 198
 
1.8%
12000 196
 
1.8%
14000 194
 
1.8%
13000 191
 
1.8%
15000 184
 
1.7%
17000 160
 
1.5%
19000 154
 
1.4%
16000 149
 
1.4%
26000 149
 
1.4%
25000 143
 
1.3%
Other values (449) 7427
68.5%
(Missing) 1695
 
15.6%
ValueCountFrequency (%)
10 10
0.1%
11 1
 
< 0.1%
14 1
 
< 0.1%
17 2
 
< 0.1%
18 2
 
< 0.1%
20 1
 
< 0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
25 1
 
< 0.1%
26 2
 
< 0.1%
ValueCountFrequency (%)
100000 16
0.1%
99000 39
0.4%
98000 16
0.1%
97000 20
0.2%
96000 26
0.2%
95000 18
0.2%
94000 17
0.2%
93000 16
0.1%
92000 15
 
0.1%
91000 22
0.2%

Installs
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15464339
Minimum0
Maximum1 × 109
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size84.8 KiB
2023-05-15T11:49:04.474717image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q11000
median100000
Q35000000
95-th percentile50000000
Maximum1 × 109
Range1 × 109
Interquartile range (IQR)4999000

Descriptive statistics

Standard deviation85029361
Coefficient of variation (CV)5.4984156
Kurtosis100.28001
Mean15464339
Median Absolute Deviation (MAD)99990
Skewness9.5720668
Sum1.6763343 × 1011
Variance7.2299923 × 1015
MonotonicityNot monotonic
2023-05-15T11:49:04.638995image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1000000 1579
14.6%
10000000 1252
11.5%
100000 1169
10.8%
10000 1054
9.7%
1000 907
8.4%
5000000 752
 
6.9%
100 719
 
6.6%
500000 539
 
5.0%
50000 479
 
4.4%
5000 477
 
4.4%
Other values (10) 1913
17.6%
ValueCountFrequency (%)
0 15
 
0.1%
1 67
 
0.6%
5 82
 
0.8%
10 386
 
3.6%
50 205
 
1.9%
100 719
6.6%
500 330
 
3.0%
1000 907
8.4%
5000 477
4.4%
10000 1054
9.7%
ValueCountFrequency (%)
1000000000 58
 
0.5%
500000000 72
 
0.7%
100000000 409
 
3.8%
50000000 289
 
2.7%
10000000 1252
11.5%
5000000 752
6.9%
1000000 1579
14.6%
500000 539
 
5.0%
100000 1169
10.8%
50000 479
 
4.4%

Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size84.8 KiB
Free
10039 
Paid
 
800

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters43356
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFree
2nd rowFree
3rd rowFree
4th rowFree
5th rowFree

Common Values

ValueCountFrequency (%)
Free 10039
92.6%
Paid 800
 
7.4%
(Missing) 1
 
< 0.1%

Length

2023-05-15T11:49:04.796491image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T11:49:04.944097image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
free 10039
92.6%
paid 800
 
7.4%

Most occurring characters

ValueCountFrequency (%)
e 20078
46.3%
F 10039
23.2%
r 10039
23.2%
P 800
 
1.8%
a 800
 
1.8%
i 800
 
1.8%
d 800
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32517
75.0%
Uppercase Letter 10839
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20078
61.7%
r 10039
30.9%
a 800
 
2.5%
i 800
 
2.5%
d 800
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
F 10039
92.6%
P 800
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 43356
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20078
46.3%
F 10039
23.2%
r 10039
23.2%
P 800
 
1.8%
a 800
 
1.8%
i 800
 
1.8%
d 800
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20078
46.3%
F 10039
23.2%
r 10039
23.2%
P 800
 
1.8%
a 800
 
1.8%
i 800
 
1.8%
d 800
 
1.8%

Price
Real number (ℝ)

SKEWED  ZEROS 

Distinct92
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0273681
Minimum0
Maximum400
Zeros10040
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size84.8 KiB
2023-05-15T11:49:05.095418image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.99
Maximum400
Range400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.949703
Coefficient of variation (CV)15.524819
Kurtosis578.143
Mean1.0273681
Median Absolute Deviation (MAD)0
Skewness23.707392
Sum11136.67
Variance254.39304
MonotonicityNot monotonic
2023-05-15T11:49:05.501952image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10040
92.6%
0.99 148
 
1.4%
2.99 129
 
1.2%
1.99 73
 
0.7%
4.99 72
 
0.7%
3.99 63
 
0.6%
1.49 46
 
0.4%
5.99 30
 
0.3%
2.49 26
 
0.2%
9.99 21
 
0.2%
Other values (82) 192
 
1.8%
ValueCountFrequency (%)
0 10040
92.6%
0.99 148
 
1.4%
1 3
 
< 0.1%
1.04 1
 
< 0.1%
1.2 1
 
< 0.1%
1.26 1
 
< 0.1%
1.29 1
 
< 0.1%
1.49 46
 
0.4%
1.5 1
 
< 0.1%
1.59 1
 
< 0.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
399.99 12
0.1%
394.99 1
 
< 0.1%
389.99 1
 
< 0.1%
379.99 1
 
< 0.1%
299.99 1
 
< 0.1%
200 1
 
< 0.1%
154.99 1
 
< 0.1%
109.99 1
 
< 0.1%
89.99 1
 
< 0.1%

Content Rating
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
Everyone
8714 
Teen
1208 
Mature 17+
 
499
Everyone 10+
 
414
Adults only 18+
 
3

Length

Max length15
Median length8
Mean length7.8008303
Min length4

Characters and Unicode

Total characters84561
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEveryone
2nd rowEveryone
3rd rowEveryone
4th rowTeen
5th rowEveryone

Common Values

ValueCountFrequency (%)
Everyone 8714
80.4%
Teen 1208
 
11.1%
Mature 17+ 499
 
4.6%
Everyone 10+ 414
 
3.8%
Adults only 18+ 3
 
< 0.1%
Unrated 2
 
< 0.1%

Length

2023-05-15T11:49:05.707457image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T11:49:05.877889image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
everyone 9128
77.6%
teen 1208
 
10.3%
mature 499
 
4.2%
17 499
 
4.2%
10 414
 
3.5%
adults 3
 
< 0.1%
only 3
 
< 0.1%
18 3
 
< 0.1%
unrated 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 21173
25.0%
n 10341
12.2%
r 9629
11.4%
y 9131
10.8%
o 9131
10.8%
E 9128
10.8%
v 9128
10.8%
T 1208
 
1.4%
919
 
1.1%
+ 916
 
1.1%
Other values (13) 3857
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70054
82.8%
Uppercase Letter 10840
 
12.8%
Decimal Number 1832
 
2.2%
Space Separator 919
 
1.1%
Math Symbol 916
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 21173
30.2%
n 10341
14.8%
r 9629
13.7%
y 9131
13.0%
o 9131
13.0%
v 9128
13.0%
t 504
 
0.7%
u 502
 
0.7%
a 501
 
0.7%
l 6
 
< 0.1%
Other values (2) 8
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
E 9128
84.2%
T 1208
 
11.1%
M 499
 
4.6%
A 3
 
< 0.1%
U 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 916
50.0%
7 499
27.2%
0 414
22.6%
8 3
 
0.2%
Space Separator
ValueCountFrequency (%)
919
100.0%
Math Symbol
ValueCountFrequency (%)
+ 916
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 80894
95.7%
Common 3667
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 21173
26.2%
n 10341
12.8%
r 9629
11.9%
y 9131
11.3%
o 9131
11.3%
E 9128
11.3%
v 9128
11.3%
T 1208
 
1.5%
t 504
 
0.6%
u 502
 
0.6%
Other values (7) 1019
 
1.3%
Common
ValueCountFrequency (%)
919
25.1%
+ 916
25.0%
1 916
25.0%
7 499
13.6%
0 414
11.3%
8 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 21173
25.0%
n 10341
12.2%
r 9629
11.4%
y 9131
10.8%
o 9131
10.8%
E 9128
10.8%
v 9128
10.8%
T 1208
 
1.4%
919
 
1.1%
+ 916
 
1.1%
Other values (13) 3857
 
4.6%

Genres
Categorical

Distinct119
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
Tools
842 
Entertainment
 
623
Education
 
549
Medical
 
463
Business
 
460
Other values (114)
7903 

Length

Max length37
Median length32
Mean length10.422048
Min length4

Characters and Unicode

Total characters112975
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.2%

Sample

1st rowArt & Design
2nd rowArt & Design;Pretend Play
3rd rowArt & Design
4th rowArt & Design
5th rowArt & Design;Creativity

Common Values

ValueCountFrequency (%)
Tools 842
 
7.8%
Entertainment 623
 
5.7%
Education 549
 
5.1%
Medical 463
 
4.3%
Business 460
 
4.2%
Productivity 424
 
3.9%
Sports 398
 
3.7%
Personalization 392
 
3.6%
Communication 387
 
3.6%
Lifestyle 381
 
3.5%
Other values (109) 5921
54.6%

Length

2023-05-15T11:49:06.063518image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2073
 
13.4%
tools 842
 
5.5%
entertainment 623
 
4.0%
education 549
 
3.6%
medical 463
 
3.0%
business 460
 
3.0%
productivity 424
 
2.7%
sports 398
 
2.6%
personalization 392
 
2.5%
communication 387
 
2.5%
Other values (125) 8836
57.2%

Most occurring characters

ValueCountFrequency (%)
i 9816
 
8.7%
o 9089
 
8.0%
e 8939
 
7.9%
n 8905
 
7.9%
t 8623
 
7.6%
a 8618
 
7.6%
s 6282
 
5.6%
4607
 
4.1%
l 4601
 
4.1%
r 4532
 
4.0%
Other values (33) 38963
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91925
81.4%
Uppercase Letter 13872
 
12.3%
Space Separator 4607
 
4.1%
Other Punctuation 2571
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9816
10.7%
o 9089
9.9%
e 8939
9.7%
n 8905
9.7%
t 8623
9.4%
a 8618
9.4%
s 6282
 
6.8%
l 4601
 
5.0%
r 4532
 
4.9%
c 4395
 
4.8%
Other values (13) 18125
19.7%
Uppercase Letter
ValueCountFrequency (%)
P 1859
13.4%
E 1782
12.8%
S 1286
9.3%
A 1141
8.2%
T 1140
8.2%
M 956
 
6.9%
B 880
 
6.3%
C 845
 
6.1%
F 836
 
6.0%
L 726
 
5.2%
Other values (7) 2421
17.5%
Other Punctuation
ValueCountFrequency (%)
& 2073
80.6%
; 498
 
19.4%
Space Separator
ValueCountFrequency (%)
4607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 105797
93.6%
Common 7178
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9816
 
9.3%
o 9089
 
8.6%
e 8939
 
8.4%
n 8905
 
8.4%
t 8623
 
8.2%
a 8618
 
8.1%
s 6282
 
5.9%
l 4601
 
4.3%
r 4532
 
4.3%
c 4395
 
4.2%
Other values (30) 31997
30.2%
Common
ValueCountFrequency (%)
4607
64.2%
& 2073
28.9%
; 498
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9816
 
8.7%
o 9089
 
8.0%
e 8939
 
7.9%
n 8905
 
7.9%
t 8623
 
7.6%
a 8618
 
7.6%
s 6282
 
5.6%
4607
 
4.1%
l 4601
 
4.1%
r 4532
 
4.0%
Other values (33) 38963
34.5%

Current Ver
Categorical

Distinct2831
Distinct (%)26.1%
Missing8
Missing (%)0.1%
Memory size84.8 KiB
Varies with device
1459 
1.0
809 
1.1
 
264
1.2
 
178
2.0
 
151
Other values (2826)
7971 

Length

Max length50
Median length47
Mean length6.8733383
Min length1

Characters and Unicode

Total characters74452
Distinct characters79
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1810 ?
Unique (%)16.7%

Sample

1st row1.0.0
2nd row2.0.0
3rd row1.2.4
4th rowVaries with device
5th row1.1

Common Values

ValueCountFrequency (%)
Varies with device 1459
 
13.5%
1.0 809
 
7.5%
1.1 264
 
2.4%
1.2 178
 
1.6%
2.0 151
 
1.4%
1.3 145
 
1.3%
1.0.0 136
 
1.3%
1.0.1 119
 
1.1%
1.4 88
 
0.8%
1.5 81
 
0.7%
Other values (2821) 7402
68.3%

Length

2023-05-15T11:49:06.255282image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
varies 1459
 
10.5%
with 1459
 
10.5%
device 1459
 
10.5%
1.0 814
 
5.8%
1.1 266
 
1.9%
1.2 178
 
1.3%
2.0 154
 
1.1%
1.3 147
 
1.1%
1.0.0 136
 
1.0%
1.0.1 120
 
0.9%
Other values (2867) 7734
55.5%

Most occurring characters

ValueCountFrequency (%)
. 15513
20.8%
1 8916
12.0%
0 5818
 
7.8%
e 4508
 
6.1%
i 4464
 
6.0%
2 4316
 
5.8%
3094
 
4.2%
3 2771
 
3.7%
4 2164
 
2.9%
5 1729
 
2.3%
Other values (69) 21159
28.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30681
41.2%
Lowercase Letter 23103
31.0%
Other Punctuation 15526
20.9%
Space Separator 3094
 
4.2%
Uppercase Letter 1762
 
2.4%
Dash Punctuation 133
 
0.2%
Connector Punctuation 61
 
0.1%
Open Punctuation 39
 
0.1%
Close Punctuation 39
 
0.1%
Math Symbol 12
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4508
19.5%
i 4464
19.3%
a 1579
 
6.8%
r 1565
 
6.8%
d 1551
 
6.7%
v 1517
 
6.6%
s 1506
 
6.5%
t 1506
 
6.5%
c 1500
 
6.5%
w 1477
 
6.4%
Other values (16) 1930
8.4%
Uppercase Letter
ValueCountFrequency (%)
V 1479
83.9%
A 31
 
1.8%
C 24
 
1.4%
R 24
 
1.4%
B 21
 
1.2%
P 21
 
1.2%
S 18
 
1.0%
E 18
 
1.0%
T 16
 
0.9%
D 15
 
0.9%
Other values (15) 95
 
5.4%
Decimal Number
ValueCountFrequency (%)
1 8916
29.1%
0 5818
19.0%
2 4316
14.1%
3 2771
 
9.0%
4 2164
 
7.1%
5 1729
 
5.6%
6 1437
 
4.7%
7 1339
 
4.4%
8 1169
 
3.8%
9 1022
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 15513
99.9%
/ 4
 
< 0.1%
, 4
 
< 0.1%
: 3
 
< 0.1%
; 1
 
< 0.1%
' 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 132
99.2%
1
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 38
97.4%
[ 1
 
2.6%
Close Punctuation
ValueCountFrequency (%)
) 38
97.4%
] 1
 
2.6%
Math Symbol
ValueCountFrequency (%)
+ 9
75.0%
| 3
 
25.0%
Space Separator
ValueCountFrequency (%)
3094
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 61
100.0%
Other Symbol
ValueCountFrequency (%)
® 1
100.0%
Other Number
ValueCountFrequency (%)
³ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49587
66.6%
Latin 24865
33.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4508
18.1%
i 4464
18.0%
a 1579
 
6.4%
r 1565
 
6.3%
d 1551
 
6.2%
v 1517
 
6.1%
s 1506
 
6.1%
t 1506
 
6.1%
c 1500
 
6.0%
V 1479
 
5.9%
Other values (41) 3690
14.8%
Common
ValueCountFrequency (%)
. 15513
31.3%
1 8916
18.0%
0 5818
 
11.7%
2 4316
 
8.7%
3094
 
6.2%
3 2771
 
5.6%
4 2164
 
4.4%
5 1729
 
3.5%
6 1437
 
2.9%
7 1339
 
2.7%
Other values (18) 2490
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74448
> 99.9%
None 3
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15513
20.8%
1 8916
12.0%
0 5818
 
7.8%
e 4508
 
6.1%
i 4464
 
6.0%
2 4316
 
5.8%
3094
 
4.2%
3 2771
 
3.7%
4 2164
 
2.9%
5 1729
 
2.3%
Other values (65) 21155
28.4%
Punctuation
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
® 1
33.3%
à 1
33.3%
³ 1
33.3%

Android Ver
Categorical

Distinct32
Distinct (%)0.3%
Missing1364
Missing (%)12.6%
Memory size84.8 KiB
4.1
2451 
4.0.3
1501 
4.0
1375 
4.4
980 
2.3
652 
Other values (27)
2517 

Length

Max length13
Median length3
Mean length3.3862389
Min length3

Characters and Unicode

Total characters32088
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st row4.0.3
2nd row4.0.3
3rd row4.0.3
4th row4.2
5th row4.4

Common Values

ValueCountFrequency (%)
4.1 2451
22.6%
4.0.3 1501
13.8%
4.0 1375
12.7%
4.4 980
 
9.0%
2.3 652
 
6.0%
5.0 601
 
5.5%
4.2 394
 
3.6%
2.3.3 281
 
2.6%
2.2 244
 
2.3%
4.3 243
 
2.2%
Other values (22) 754
 
7.0%
(Missing) 1364
12.6%

Length

2023-05-15T11:49:06.416059image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4.1 2452
25.8%
4.0.3 1503
15.8%
4.0 1375
14.5%
4.4 980
 
10.3%
2.3 652
 
6.9%
5.0 605
 
6.4%
4.2 394
 
4.1%
2.3.3 281
 
3.0%
2.2 245
 
2.6%
4.3 243
 
2.6%
Other values (17) 764
 
8.0%

Most occurring characters

ValueCountFrequency (%)
. 11282
35.2%
4 7951
24.8%
0 3877
 
12.1%
3 3247
 
10.1%
1 2780
 
8.7%
2 2026
 
6.3%
5 649
 
2.0%
6 177
 
0.6%
7 52
 
0.2%
18
 
0.1%
Other values (3) 29
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20767
64.7%
Other Punctuation 11282
35.2%
Space Separator 18
 
0.1%
Uppercase Letter 12
 
< 0.1%
Dash Punctuation 9
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7951
38.3%
0 3877
18.7%
3 3247
15.6%
1 2780
 
13.4%
2 2026
 
9.8%
5 649
 
3.1%
6 177
 
0.9%
7 52
 
0.3%
8 8
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 11282
100.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Uppercase Letter
ValueCountFrequency (%)
W 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32076
> 99.9%
Latin 12
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 11282
35.2%
4 7951
24.8%
0 3877
 
12.1%
3 3247
 
10.1%
1 2780
 
8.7%
2 2026
 
6.3%
5 649
 
2.0%
6 177
 
0.6%
7 52
 
0.2%
18
 
0.1%
Other values (2) 17
 
0.1%
Latin
ValueCountFrequency (%)
W 12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 11282
35.2%
4 7951
24.8%
0 3877
 
12.1%
3 3247
 
10.1%
1 2780
 
8.7%
2 2026
 
6.3%
5 649
 
2.0%
6 177
 
0.6%
7 52
 
0.2%
18
 
0.1%
Other values (3) 29
 
0.1%

Last Updated day
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.609041
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.8 KiB
2023-05-15T11:49:06.562377image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median16
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.5616206
Coefficient of variation (CV)0.6125694
Kurtosis-1.3416525
Mean15.609041
Median Absolute Deviation (MAD)9
Skewness-0.0025692323
Sum169202
Variance91.424588
MonotonicityNot monotonic
2023-05-15T11:49:06.725865image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3 603
 
5.6%
2 535
 
4.9%
1 499
 
4.6%
6 450
 
4.2%
26 432
 
4.0%
25 425
 
3.9%
31 425
 
3.9%
30 419
 
3.9%
27 399
 
3.7%
5 390
 
3.6%
Other values (21) 6263
57.8%
ValueCountFrequency (%)
1 499
4.6%
2 535
4.9%
3 603
5.6%
4 332
3.1%
5 390
3.6%
6 450
4.2%
7 266
2.5%
8 228
 
2.1%
9 312
2.9%
10 231
 
2.1%
ValueCountFrequency (%)
31 425
3.9%
30 419
3.9%
29 273
2.5%
28 272
2.5%
27 399
3.7%
26 432
4.0%
25 425
3.9%
24 370
3.4%
23 354
3.3%
22 281
2.6%

Last Updated month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4223247
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.8 KiB
2023-05-15T11:49:06.887528image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median7
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5783881
Coefficient of variation (CV)0.40147271
Kurtosis-0.042104493
Mean6.4223247
Median Absolute Deviation (MAD)1
Skewness-0.11444239
Sum69618
Variance6.6480854
MonotonicityNot monotonic
2023-05-15T11:49:07.021400image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 3163
29.2%
8 1594
14.7%
6 1273
11.7%
5 978
 
9.0%
3 667
 
6.2%
4 616
 
5.7%
2 533
 
4.9%
1 491
 
4.5%
12 426
 
3.9%
10 398
 
3.7%
Other values (2) 701
 
6.5%
ValueCountFrequency (%)
1 491
 
4.5%
2 533
 
4.9%
3 667
 
6.2%
4 616
 
5.7%
5 978
 
9.0%
6 1273
11.7%
7 3163
29.2%
8 1594
14.7%
9 314
 
2.9%
10 398
 
3.7%
ValueCountFrequency (%)
12 426
 
3.9%
11 387
 
3.6%
10 398
 
3.7%
9 314
 
2.9%
8 1594
14.7%
7 3163
29.2%
6 1273
11.7%
5 978
 
9.0%
4 616
 
5.7%
3 667
 
6.2%

Last Updated year
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.3997
Minimum2010
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.8 KiB
2023-05-15T11:49:07.161983image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2015
Q12017
median2018
Q32018
95-th percentile2018
Maximum2018
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1009142
Coefficient of variation (CV)0.00054570951
Kurtosis5.7158323
Mean2017.3997
Median Absolute Deviation (MAD)0
Skewness-2.2882932
Sum21868613
Variance1.2120121
MonotonicityNot monotonic
2023-05-15T11:49:07.301630image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2018 7349
67.8%
2017 1867
 
17.2%
2016 804
 
7.4%
2015 459
 
4.2%
2014 209
 
1.9%
2013 110
 
1.0%
2012 26
 
0.2%
2011 15
 
0.1%
2010 1
 
< 0.1%
ValueCountFrequency (%)
2010 1
 
< 0.1%
2011 15
 
0.1%
2012 26
 
0.2%
2013 110
 
1.0%
2014 209
 
1.9%
2015 459
 
4.2%
2016 804
 
7.4%
2017 1867
 
17.2%
2018 7349
67.8%
ValueCountFrequency (%)
2018 7349
67.8%
2017 1867
 
17.2%
2016 804
 
7.4%
2015 459
 
4.2%
2014 209
 
1.9%
2013 110
 
1.0%
2012 26
 
0.2%
2011 15
 
0.1%
2010 1
 
< 0.1%

Interactions

2023-05-15T11:49:00.433538image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:50.633073image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:52.201122image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:53.553360image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:54.841807image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:56.189214image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:57.584223image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:58.923027image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:49:00.618260image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:50.829251image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:52.378844image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:53.726521image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:55.025743image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:56.372493image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:57.772751image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:59.093555image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:49:00.800500image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:51.147733image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:52.554246image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:53.873221image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:55.197431image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:56.558780image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:57.944653image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:59.260119image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:49:00.962795image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:51.313831image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:52.706689image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:54.022897image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:55.354240image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:56.728319image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:58.099109image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:59.623001image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:49:01.128358image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:51.484887image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:52.869888image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:54.182741image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:55.510510image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:56.896050image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:58.257199image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:59.781700image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:49:01.309411image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:51.685231image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:53.047633image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:54.351744image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:55.692935image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:57.067832image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:58.428772image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:59.949878image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:49:01.477781image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:51.857632image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:53.212659image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:54.513115image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:55.859816image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:57.234416image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:58.587440image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:49:00.107206image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:49:01.650272image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:52.018642image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:53.373603image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:54.669847image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:56.016396image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:57.397671image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:48:58.748888image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-05-15T11:49:00.261577image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Correlations

2023-05-15T11:49:07.456330image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
RatingReviewsSizeInstallsPriceLast Updated dayLast Updated monthLast Updated yearCategoryTypeContent RatingAndroid Ver
Rating1.0000.1560.0700.0700.064-0.0190.0320.1720.0850.0710.0400.055
Reviews0.1561.0000.3680.971-0.171-0.0430.1420.2760.0720.0170.0620.000
Size0.0700.3681.0000.348-0.054-0.0050.0550.2910.1610.0500.1040.092
Installs0.0700.9710.3481.000-0.243-0.0400.1320.2800.1240.0280.0330.012
Price0.064-0.171-0.054-0.2431.000-0.001-0.014-0.1650.0480.1690.0000.000
Last Updated day-0.019-0.043-0.005-0.040-0.0011.000-0.150-0.0230.0550.0470.0320.050
Last Updated month0.0320.1420.0550.132-0.014-0.1501.000-0.2240.0920.0990.0530.108
Last Updated year0.1720.2760.2910.280-0.165-0.023-0.2241.0000.0820.1870.0730.273
Category0.0850.0720.1610.1240.0480.0550.0920.0821.0000.1980.3470.085
Type0.0710.0170.0500.0280.1690.0470.0990.1870.1981.0000.0480.192
Content Rating0.0400.0620.1040.0330.0000.0320.0530.0730.3470.0481.0000.052
Android Ver0.0550.0000.0920.0120.0000.0500.1080.2730.0850.1920.0521.000

Missing values

2023-05-15T11:49:01.942177image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-15T11:49:02.313942image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-15T11:49:02.591734image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AppCategoryRatingReviewsSizeInstallsTypePriceContent RatingGenresCurrent VerAndroid VerLast Updated dayLast Updated monthLast Updated year
0Photo Editor & Candy Camera & Grid & ScrapBookART_AND_DESIGN4.115919000.010000Free0.0EveryoneArt & Design1.0.04.0.3712018
1Coloring book moanaART_AND_DESIGN3.996714000.0500000Free0.0EveryoneArt & Design;Pretend Play2.0.04.0.31512018
2U Launcher Lite – FREE Live Cool Themes, Hide AppsART_AND_DESIGN4.7875108700.05000000Free0.0EveryoneArt & Design1.2.44.0.3182018
3Sketch - Draw & PaintART_AND_DESIGN4.521564425000.050000000Free0.0TeenArt & DesignVaries with device4.2862018
4Pixel Draw - Number Art Coloring BookART_AND_DESIGN4.39672800.0100000Free0.0EveryoneArt & Design;Creativity1.14.42062018
5Paper flowers instructionsART_AND_DESIGN4.41675600.050000Free0.0EveryoneArt & Design1.02.32632017
6Smoke Effect Photo Maker - Smoke EditorART_AND_DESIGN3.817819000.050000Free0.0EveryoneArt & Design1.14.0.32642018
7Infinite PainterART_AND_DESIGN4.13681529000.01000000Free0.0EveryoneArt & Design6.1.61.14.21462018
8Garden Coloring BookART_AND_DESIGN4.41379133000.01000000Free0.0EveryoneArt & Design2.9.23.02092017
9Kids Paint Free - Drawing FunART_AND_DESIGN4.71213100.010000Free0.0EveryoneArt & Design;Creativity2.84.0.3372018
AppCategoryRatingReviewsSizeInstallsTypePriceContent RatingGenresCurrent VerAndroid VerLast Updated dayLast Updated monthLast Updated year
10830payermonstationnement.frMAPS_AND_NAVIGATIONNaN389800.05000Free0.0EveryoneMaps & Navigation2.0.148.04.01362018
10831FR TidesWEATHER3.81195582.0100000Free0.0EveryoneWeather6.02.11622014
10832Chemin (fr)BOOKS_AND_REFERENCE4.844619.01000Free0.0EveryoneBooks & Reference0.82.22332014
10833FR CalculatorFAMILY4.072600.0500Free0.0EveryoneEducation1.0.04.11862017
10834FR FormsBUSINESSNaN09600.010Free0.0EveryoneBusiness1.1.54.02992016
10835Sya9a Maroc - FRFAMILY4.53853000.05000Free0.0EveryoneEducation1.484.12572017
10836Fr. Mike Schmitz Audio TeachingsFAMILY5.043600.0100Free0.0EveryoneEducation1.04.1672018
10837Parkinson Exercices FRMEDICALNaN39500.01000Free0.0EveryoneMedical1.02.22012017
10838The SCP Foundation DB fr nn5nBOOKS_AND_REFERENCE4.5114NaN1000Free0.0Mature 17+Books & ReferenceVaries with deviceNaN1912015
10839iHoroscope - 2018 Daily Horoscope & AstrologyLIFESTYLE4.539830719000.010000000Free0.0EveryoneLifestyleVaries with deviceNaN2572018

Duplicate rows

Most frequently occurring

AppCategoryRatingReviewsSizeInstallsTypePriceContent RatingGenresCurrent VerAndroid VerLast Updated dayLast Updated monthLast Updated year# duplicates
59CBS Sports App - Scores, News, Stats & Watch LiveSPORTS4.391031NaN5000000Free0.0EveryoneSportsVaries with device5.04820184
159Google KeepPRODUCTIVITY4.4691474NaN100000000Free0.0EveryoneProductivityVaries with deviceNaN6820184
253NickENTERTAINMENT4.212327925000.010000000Free0.0Everyone 10+Entertainment;Music & Video2.0.84.424120184
285Quizlet: Learn Languages & Vocab with FlashcardsEDUCATION4.6211856NaN10000000Free0.0EveryoneEducationVaries with deviceNaN1820184
316SkyscannerTRAVEL_AND_LOCAL4.548154629000.010000000Free0.0EveryoneTravel & Local5.484.46820184
372WatchESPNSPORTS4.12888096600.010000000Free0.0EveryoneSports2.5.14.427920174
395eBay: Buy & Sell this Summer - Discover Deals Now!SHOPPING4.42788923NaN100000000Free0.0TeenShoppingVaries with deviceNaN30720184
7A&E - Watch Full Episodes of TV ShowsENTERTAINMENT4.02970619000.01000000Free0.0TeenEntertainment3.1.44.416720183
14Adult Dirty EmojisDATING2.8805500.010000Free0.0TeenDating1.04.0.361120173
28BBC NewsNEWS_AND_MAGAZINES4.3296781NaN10000000Free0.0Everyone 10+News & MagazinesVaries with deviceNaN24720183